Until recently, i-vectors were the most commonly used means for representing speaker characteristics [1]}, [2]}. With the advent of deep learning, neural architectures such as time-delay neural networks (TDNNs [3]}), Long Short-Term Memory networks (LSTMs), 2D convolutional neural networks (such as ResNets and its extensions [4]}) and Transformers have emerged, surpassing the performance of i-vectors in most applications and speaker recognition settings.
